Tracking the motion of a human has been an important and open problem in many applications such as biomedical, special sports, clinical applications and entertainment. Among all the motion tracking technologies, integrated Inertial Measurement Unit (IMU) orientation sensors are currently used widely for spatial orientation tracking. Due to demand and advancements in manufacturing technology, IMU sensors have become smaller and cheaper with significant improvements in accuracy and reliability in human measurement. Over the next few years, IMU sensors may become the main orientation sensor used in motion tracking systems and related products for cinematic production, digital entertainment, high performance sports, and medical rehabilitation. For example, see J. Favre, B. Jolles, R. Aissaoui, and K. Aminian, “Ambulatory measurement of 3D knee joint angle,” Journal of biomechanics, vol. 41, pp. 1029-1035, 2008; M. Brodie, A. Walmsley, and W. Page, “Fusion motion capture: a prototype system using inertial measurement units and GPS for the biomechanical analysis of ski racing,” Sports Technology, vol. 1, pp. 17-28, 2008; Qilong Yuan, I-Ming Chen, and S. P. Lee, “SLAC: 3D Localization of Human Based on Kinetic Human Movement Capture,” in Robotics and Automation (ICRA), 2011 IEEE International Conference on, Shanghai, China, 2011; C. Frigo, M. Rabuffetti, D. Kerrigan, L. Deming, and A. Pedotti, “Functionally oriented and clinically feasible quantitative gait analysis method,” Medical and Biological Engineering and Computing, vol. 36, pp. 179-185, 1998; C. Goodvin, E. Park, K. Huang, and K. Sakaki, “Development of a real-time three-dimensional spinal motion measurement system for clinical practice,” Medical and Biological Engineering and Computing, vol. 44, pp. 1061-1075, 2006; E. Bachmann, “Inertial and magnetic tracking of limb segment orientation for inserting humans into synthetic environments.,” PhD Thesis, Naval Postgraduate School Monterey Calif., 2000.
However, to use IMU sensors to recover the motion of users from the sensor outputs or the orientation signals of the sensor, a calibration procedure is typically necessary to set the ground truth of the motion tracking system and subsequently optimise the performance of the motion tracking devices. Any motion tracking system or product of this nature requires a method and/or software for motion calibration.
Existing calibration methods depend on external measurement devices and regression analysis of the collected data. This means, these calibration procedures are very time consuming and processor intensive. In the calibration, the mappings from sensor orientations to the corresponding body segment orientations and the body segment dimensions are the parameters to be determined. For wearable motion tracking devices, after attaching all the sensors on the required body segments of a user, the mappings between the body segment orientation and the corresponding sensor orientation need to be calibrated to estimate the body movements of the user.
Even though the mapping calibration procedure for inertial motion capture (IMC) systems is less complicated than for optical, magnetic and ultrasonic based motion tracking systems, existing IMC systems still have many restrictions on the user (or subject), and are usually very heavily dependent on external devices.
For example, in the calibration procedures of Gypsy 190® IMC systems of MetaMotion® (e.g. see MetaMotion. (2010). http://www.metamotion.com) and MVN® systems of Xsens® (e.g. see X. Technologies. (2010). http://www.xsens.com and D. Roetenberg, H. Luinge, and P. Slycke, “Xsens MVN: full 6DOF human motion tracking using miniature inertial sensors,” Xsens Technologies, 2009.), the users are required to face the magnetic north with a standing posture or “T” posture during the calibration. In this way, the body bearing orientation is known for calibration. However, the reference-north orientation is not always available, and a user cannot match the desired body bearing orientation exactly. More importantly, when the user conducts motion capture sessions for long durations, the sensors will move slightly on the body segments. In this case, recalibration of the system will be needed to continue precise motion capture. It is desirable to have a reliable and convenient procedure that can be conducted at anytime without and special requirements of the users (e.g. special range of motion or north orientations).
Besides the mapping, the skeleton dimensions are also very critical for precise motion representation. In many human postures or movements in surroundings, the skeleton model of the user needs to be precise enough to provide the exact posture or movements. For example, when the user holds their hands together, if the skeleton model is not accurate, in the captured posture, the two hands of the skeleton model may intersect with or separate from each other. When walking, if the estimated skeleton dimensions of the lower limb are smaller than the actual ones, the captured step length for walking is also shortened which leads to inaccurate body translation. When a user is sitting on a chair, the captured result may show they are not in contact with the chair if the skeleton model is estimated longer than the actual one. Before conducting human motion tracking, a skeleton calibration procedure is required to determine the skeleton dimension parameters for initialising motion tracking systems.
For precise motion tracking, measuring the body dimensions of the human has always been a challenging issue for all kinds of motion tracking systems (also known as Mocaps). Optical based motion tracking systems can estimate the body dimensions of the user by determining the center of rotation (COR) and axis of rotation (AOR) based on the statistic analysis of position of the markers attached on human body. However, these systems require a large number of markers and a large amount of data, which makes the estimation procedure very time consuming, complicated, and processor intensive (e.g. see M. Silaghi, R. Plankers, R. Boulic, P. Fua, and D. Thalmann, “Local and global skeleton fitting techniques for optical motion capture,” Modelling and Motion Capture Techniques for Virtual Environments, pp. 26-40, 1998; or S. Gamage and J. Lasenby, “New least squares solutions for estimating the average centre of rotation and the axis of rotation,” Journal of biomechanics, vol. 35, pp. 87-94, 2002). If the marker sets are not properly assigned, the result is usually inaccurate because of the skin movement effect and COR estimation algorithms. The estimated skeleton results also have asymmetric problems (skeleton dimension are not symmetric between the left and the right side). In addition, the high price of the optical motion capture systems makes them unattractive for the general public.
Some existing commercial inertial Mocaps systems determine the body skeleton dimensions based on external measurement devices. The Gypsy® system described above measures the skeleton model by taking a front and a side view of the user. The picture is then post-processed through a software called AutoCAL® to estimate the skeleton of the subject. Brodie et al built a 3-D anthropometry for skeleton dimensions measurement of subjects (e.g. see also M. A. D. Brodie, “Development of fusion motion capture for optimization of performance in alpine ski racing,” PhD Thesis, Massey University, 2009). However, with more than ten parameters to adjust the system is complicated and time consuming for measuring the whole body skeleton dimensions. This cannot be performed by a single user, assistance is required. Post-processing the data to generate the skeleton model also takes time, and so is not suitable for real-time applications.
It is desirable to have a convenient, fast, and flexible calibration procedure that can reduce system set-up time of motion tracking systems. With quick calibration, users can run their motion tracking applications more efficiently anywhere and anytime. Therefore, there is a significant need to provide methods, apparatus and/or mechanisms for efficient and simple calibration/recalibration of motion tracking systems.